基于自适应响应面法的输电塔线体系腐蚀疲劳可靠度研究

环境腐蚀和风振疲劳耦合作用下输电塔体的结构性能逐渐退化,满足预定设计功能的概率减小。然而,传统响应面法计算结构可靠度时均不能兼顾"效率"和"精度"。为此,首先通过严格的数学推导给出了交叉项是否存在的判断准则,将该判断准则与传统二次响应面法相结合建立了考虑部分交叉项的自适应响应面法。然后,通过Q345等边角钢腐蚀疲劳试验结果给出了构件腐蚀疲劳t-P-S-N曲线方程,再与概率论相结合建立了随机疲劳曲线方程。最后,通过工程算例采用建议自适应响应面法以风速、腐蚀时间和随机腐蚀疲劳S-N曲线方程为随机变量对输电塔线体系进行了腐蚀疲劳可靠度研究,结果表明:①交叉项判断准则能有效地保留相互影响随机变量之间的交叉项;②建议自适应响应面法在满足精度的同时能有效减少计算量;③构件随机腐蚀疲劳S-N曲线模型在结构可靠性分析中简单易行。     

基于等概率近似变换的 向量型层递响应面法分析结构可靠度

基于正交变换和等概率近似变换,研究建立了随机变量为非高斯互相关的工程结构可靠度分析的向量型层递响应面法。首先利用正交变换将非高斯互相关随机变量变换为互不相关的非高斯标准随机变量,建立结构总体刚度矩阵和荷载列阵,据此定义预处理器并形成预处理随机Krylov子空间,进而利用该空间的层递基向量将结构总体节点位移向量近似展开,建立关于互不相关非高斯标准随机变量的层递响应面;然后根据等概率近似变换,将独立标准正态空间的样本点转换为层递响应面在非高斯空间中的概率配点;最后通过回归分析确定层递响应面待定系数,并利用层递响应面建立极限状态方程求解结构可靠度。分析表明:该文提出的等概率近似变换方法不仅成功地将层递响应面法拓展到非高斯互相关随机变量下的结构可靠度分析,而且方法简便、适用范围广、计算精度和效率较高,具有良好的全域性。     

随机结构在随机载荷下的动力可靠度分析

提出了综合考虑动载荷和结构参数双重随机性的动力可靠度分析方法.首先基于随机振动的首次超越破坏准则,参照随机变量功能函数模式,建立了随机结构动力可靠度功能函数;然后引入序列响应面法,对功能函数进行拟合;最后用JC法求解可靠指标.算例分析验证了本文方法具有较好的计算精度和计算效率,尤其在复杂工程结构的动力可靠度分析中有广阔的应用前景.     

非线性隐式极限状态方程失效概率计算的组合响应面法

提出组合响应面的新方法,用以计算设计点附近非线性程度较大的隐式极限状态方程的失效概率。该方法用主响应面和多个次响应面近似对失效概率贡献较大的区域,其响应面函数形式为不含交叉的二次多项式。主响应面依据传统响应面法通过选择适当的插值点和迭代运算获得,其设计点为主设计点。延坐标轴正负方向偏移主设计点得到拟均值点。以拟均值点为基础得到一组次响应面和次设计点。通过主次响应面在各自设计点处的切平面建立组合响应面近似原隐式极限状态方程,并计算其失效概率。算例结果说明所提方法具有较高精度。     

基于分片响应面的钢筋混凝土梁变形随机模拟

将响应面与蒙特卡洛方法结合,可降低随机模拟的计算代价。但当钢筋混凝土梁所受荷载接近开裂荷载时,梁体的变形响应面形状将出现不光滑的现象。采用传统的单一响应面方法来描述结构响应将与实际产生明显的偏差。采用改进的分片响应面技术结合蒙特卡洛方法,建立了钢筋混凝土梁变形随机分析模型。以钢筋混凝土试验梁作为算例,讨论了分片参数的合理取值,并验证了方法的正确性。     

爆炸载荷作用下弹性结构动力可靠性研究

为了研究随机爆炸载荷对弹性结构安全性能的影响,基于有限体积法（FVM）发展了一种爆炸载荷作用下弹性结构动力可靠度的计算方法。基于FVM的控制方程推导出响应量对基本随机变量偏导的显式求解方程;将响应量在随机变量的均值点处进行泰勒展开,略去高阶项,并在展开式两端同时取均值和方差可求得响应量的均值和方差等数字特性;在此基础上利用一次二阶矩法求解结构的可靠性指标。为了证明该方法的可行性,考虑炸药的装药量、爆距及结构的屈服应力作为基本随机变量,计算了固支板在远场爆炸载荷作用下的可靠度,并分别与响应面法和蒙特卡洛法做了对比,结果表明该方法具有较高的效率和精度,能够为工程实践提供参考。     

一种新的非高斯随机振动疲劳寿命估计方法

构造了非高斯修正系数的多项式响应面模型,提出了一种基于高斯近似的非高斯随机振动疲劳寿命估计方法。采用Winterstein传递函数法将非高斯随机应力转化成高斯随机应力,并联合雨流计数和Miner损伤准则分别估算两种随机应力下的累计损伤和谱矩,对多个样本进行最小二乘拟合之后构建一个关于应力谱矩和非高斯修正系数的多项式响应面模型。利用高斯近似法估算非高斯随机振动疲劳损伤量,并与经过雨流计数和Miner损伤准则估算的非高斯随机过程下疲劳损伤对比,结果表明:高斯近似法具有较好的精度。     

基于单变量分析的自适应响应面法

由于简便易行,响应面法在结构可靠度分析中获得了众多关注,其中尤以不考虑交叉项的二次响应面应用最为广泛。然而,对于与强非线性相对应的复杂极限状态曲面,二次响应面的近似精度显然不够,从而引起可靠度估计的较大误差。虽然理论上高次响应面法可以解决这一问题,但待定系数的急剧增长导致计算效率降低甚至无法实现。为此,该文以多变量函数的单变量分析为基础,提出了一种合理确定高次响应面形式的算法,可以有效地减少响应面中待定系数的数量,降低了计算的困难,并且针对可能出现的问题提出了改进措施;此外,基于数论选点策略,发展了一类适用于响应面法的数论选点方案。最后,通过算例分析对建议算法进行验证,结果表明该算法具有较好的精度和效率。     

一种高效的结构可靠度近似分析方法

本文简要讨论了结构的可靠度计算的数值模拟方法,如Monte-Carlo法、响应面和两点近似法;结合结构分析的确定性有限元法及可靠度计算的一次二阶矩法,提出了一种结构可靠度分析的近似方法;算例表明,当荷载的不确定性远大于结构的不确定性时,本文提出的方法满足工程精度要求,从而大大简化了结构可靠度分析过程。     

基于逆响应面法的有限元模型修正

基于响应面法（RSM）的有限元模型修正是以若干设计参数（如密度、弹性模量等）为自变量,以若干特征参数（如固有频率、振型等）为因变量,通过回归分析方法来拟合特征参数关于修正参数的显式表达式。提出的逆响应面法（IRSM）则是以特征参数作为自变量,设计参数作为因变量。利用此法的有限元模型修正可直接根据特征参数的目标值得到设计参数的修正量,而不需要经过迭代计算,有效地提高计算速度和精度。介绍逆响应面法及其应用,讨论使用响应面法和逆响应面法进行有限元模型修正的优缺点和适用范围,分析适合于逆响应面法的逆响应面函数、实验设计方案和回归精度检验的方法。利用逆响应面法对一简支梁进行有限元模型修正的结果表明,逆响应面法能高效准确地修正设计参数,对于输出变量少于输入变量的情况更能显著减少有限元计算次数,适用于复杂的工程结构。     

Reliability analysis of structures using neural network method

In order to predict the failure probability of a complicated structure, the structural responses usually need to be estimated by a numerical procedure, such as finite element method. To reduce the computational effort required for reliability analysis, response surface method could be used. However the conventional response surface method is still time consuming especially when the number of random variables is large. In this paper, an artificial neural network (ANN)-based response surface method is proposed. In this method, the relation between the random variables (input) and structural responses is established using ANN models. ANN model is then connected to a reliability method, such as first order and second moment (FORM), or Monte Carlo simulation method (MCS), to predict the failure probability. The proposed method is applied to four examples to validate its accuracy and efficiency. The obtained results show that the ANN-based response surface method is more efficient and accurate than the conventional response surface method.     

利用矩点估计法简化响应面可靠度指标的计算

针对响应面可靠度指标计算方法的缺陷,将罗森布鲁斯统计矩点估计法引入到经典响应面方法中对其进行改进。改进后响应面方法,考虑基本随机变量偏态情况,改变了模拟试验中随机变量抽样值的计算方法;直接采用了统计矩计算可靠度指标,使可靠度指标的计算在真实极限状态曲面的某些特殊点上进行;在计算过程中不需拟合近似极限状态曲面和对非线性方程进行线性化处理,不产生迭代误差和累积误差,计算过程简洁明了。最后分别利用改进的响应面方法和经典响应面方法分析了某一矿山大型工程的稳定可靠性,并以蒙特卡洛法计算结果作为准精确解进行了比较,计算结果满足工程要求。     

结构随机分析的向量型层递响应面法

结构随机分析的传统响应面法通常用于显化表示某一随机响应量在结构特定点的涨落,属于标量型响应面法。为克服传统响应面法的局限性,提出了结构总体节点位移向量显化表示的层递响应面法,属于向量型响应面法。首先利用Karhunen-Loève级数线性展开随机刚度矩阵和节点荷载向量,并定义随机场均值处的刚度矩阵作为预处理器,进而生成预处理Krylov子空间;然后将结构总体节点位移向量在该空间中层递展开,建立向量型的层递响应面,有效保持了节点位移之间的协调性;最后分析了层递响应面与混沌多项式之间的关系,给出了样本点选取原则,建立了节点位移向量的均值和协方差计算公式。通过算例分析,验证了层递响应面法的高精度、全域性和快速收敛性。     

Application of the response surface methods to solve inverse reliability problems with implicit response functions

The inverse first-order reliability method (FORM) is considered to be one of the most widely used methods in inverse reliability analysis. It has been recognized that there are shortcomings of the inverse FORM in solving inverse reliability problems with implicit response functions, primarily inefficiency and difficulties involved in evaluating derivatives of the implicit response functions with respect to random variables. In order to apply the inverse FORM to structural inverse reliability analysis, response surface methods can be used to overcome the shortcomings. In the present paper, two different response surface methods, namely the polynomial-based response surface method and the artificial neural network-based response surface method, are developed to solve the inverse reliability problems with implicit response functions, and the accuracy and efficiency of the two response surface methods are demonstrated through two numerical examples of steel structures. It is found that the polynomial-based response surface method is more efficient and accurate than the artificial neural network-based response surface method. Recommendations are made regarding the suitability of the two response surface methods to solve the inverse reliability problems with implicit response functions.     

基于Kriging改进响应面法的桥梁地震动力可靠度研究

鉴于桥梁结构对地震响应的非线性和复杂性等问题,提出了基于Kriging改进响应面法的桥梁结构地震动力可靠度分析方法。首先利用Kriging模型的优越模拟性能,将其作为响应面函数,并采取自适应策略加以改进,而后采取线性过滤器脉冲响应法对地震随机激励荷载进行了离散,并基于首次超越问题的定义建立了动力可靠度极限状态方程,最后对桥梁结构的地震可靠度问题进行了分析。计算分析结果表明:提出的基于Kriging模型的改进响应面法能有效完成桥梁地震动力可靠度的计算分析(包括结构参数随机性对桥梁动力可靠度的影响),且结果具有较高的准确性和高效性。     

考虑交叉项的自适应响应面法

“精度”和“效率”是近似方法的重要评价指标。传统的二次多项式响应面法,无论是不含交叉项的二次多项式还是完全二次多项式均不能兼顾“效率”和“精度”。为此,该文中提出了一类可在两者之间达到较好平衡的自适应响应面法。一方面,为确保响应面形式更具合理性,通过严格的数学推导给出了极限状态曲面中交叉项是否存在的判断准则,将该准则与完全二次多项式相结合即可确定合理的、自适应的响应面形式;另一方面,针对该判断准则,构造了与之对应的实现算法,并结合可靠度问题的特点,将算法进一步完善;为克服此算法选点中心位于均值点的特性,引入了样本点选取的迭代方案对其改进。最后,该文中通过一个数学算例和一个工程算例分别对建议方法及算法进行验证,结果表明:1) 交叉项存在的判断准则准确、有效;2) 对于较为简单的二次极限状态曲面,建议方法可以真实还原;3) 对于涉及一般极限状态曲面的可靠度问题,建议方法具有颇为理想的精度和较高的效率。     

An efficient response surface method using moving least squares approximation for structural reliability analysis

The response surface method (RSM) is widely adopted for structural reliability analysis because of its numerical efficiency. However, the RSM is time consuming for large-scale applications and sometimes shows large errors in the calculation of the sensitivity of the reliability index with respect to random variables. In order to overcome these problems, this study proposes an efficient RSM applying a moving least squares (MLS) approximation instead of the traditional least squares approximation generally used in the RSM. The MLS approximation gives higher weight to the experimental points closer to the most probable failure point (MPFP), which allows the response surface function (RSF) to be closer to the limit state function at the MPFP. In the proposed method, a linear RSF is constructed at first and a quadratic RSF is formed using the axial experimental points selected from the reduced region where the MPFP is likely to exist. The RSF is updated successively by adding one new experimental point to the previous set of experimental points. Numerical examples are presented to demonstrate the improved accuracy and computational efficiency of the proposed method compared to the conventional RSM.     

The dimension-reduction strategy via mapping for probability density evolution analysis of nonlinear stochastic systems

Dynamic response analysis of nonlinear structures involving random parameters has for a long time been an important and challenging problem. In recent years, the probability density evolution method, which is capable of capturing the instantaneous probability density function (PDF) of the dynamic response and its evolution, has been proposed and developed for nonlinear stochastic dynamical systems. In the probability density evolution method, the strategy of selecting representative points is of critical importance to the efficiency especially when the number of random parameters is large. Enlightened by Cantor’s set theory, a strategy of dimension-reduction via mapping is proposed in the present paper. In the strategy, a two-dimensional domain is firstly considered and discretized such that the grid points are assigned with probabilities associated to the joint PDF. These points are then sorted and set on a virtual line according to a certain principle. Partitioning the sorted points on the virtual line into a certain number of intervals and selecting one single point in each interval, the two random variables can be transformed to a single comprehensive random variable. The associated probability of each point is simultaneously transformed accordingly. In the case of multiple random parameters, the above dimension-reduction procedure from two to one could be used recursively such that the random vector is finally transformed to one single comprehensive random variable. Numerical examples are investigated, showing that the proposed method is of high efficiency and fair accuracy.